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We propose novel mixed-integer linear programming (MIP) formulations to model decision problems posed as influence diagrams. We also present a novel heuristic that can be employed to warm start the MIP solver and provide heuristic solutions to more computationally challenging problems. We provide computational results showcasing the superior performance of these improved formulations as well as the performance of the proposed heuristic. Lastly, we describe a novel case study showcasing decision programming as an alternative framework for modelling multi-stage stochastic dynamic programming problems.

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Solving Influence Diagrams: Efficient Mixed-Integer Programming Formulation and Heuristic

  • 会议论文

  • 发表日期：

    2026年

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  • Helmi Hankimaa,
  • Olli Herrala,
  • Fabricio Oliveira,
  • Jaan Tollander de Balsch

摘要

We propose novel mixed-integer linear programming (MIP) formulations to model decision problems posed as influence diagrams. We also present a novel heuristic that can be employed to warm start the MIP solver and provide heuristic solutions to more computationally challenging problems. We provide computational results showcasing the superior performance of these improved formulations as well as the performance of the proposed heuristic. Lastly, we describe a novel case study showcasing decision programming as an alternative framework for modelling multi-stage stochastic dynamic programming problems.